* refactor(types): replace Any with proven types in 84 files
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(types): keep vector store search locals untyped
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(types): keep runtime payload validation out of the sweep
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(types): keep only annotation-only Any fixes
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(caching): baseline breaker metrics from live state after gc
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(caching): collect garbage before the services logger gauge test reads live breakers
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* feat(minimax): add MiniMax-H3 and H3-Max video generation via /v1/videos
MinimaxVideoConfig maps litellm's OpenAI Videos API onto MiniMax's V2 task
API. Create, status, list, content and delete hit /v2/video_generation and
/v2/query/video_generation. Remix is not exposed: MiniMax's
/v2/video_regeneration only upscales a finished 768P task to 2K and ignores any
prompt, so it raises instead of returning a video that silently drops the edit
OpenAI params translate to MiniMax's contract: seconds becomes duration, size
reduces via gcd to a valid ratio, and text-to-video requests default to ratio
16:9, resolution 768P and duration 5 when unset. input_reference becomes a
first_frame content item, base64 encoded into a data URI for file inputs, and
multimodal reference media passes through a content array in extra_body. Caller
content is validated strictly as image, video or audio items, and the scanned
prompt is always the one text item sent, so text can't reach MiniMax past the
guardrails; anything else in content returns a 400. api_base
shares MINIMAX_API_BASE with the chat config (a trailing /v1 or /v2 is stripped
because the video API carries its own version). The list endpoint pages by
number with no cursor or sort, so after and an ascending order are rejected
with a 400 rather than ignored, and page_num passes through extra_query. Responses
validate into frozen pydantic models at the boundary, video ids are
provider-encoded so status and content route back to MiniMax, and create
reports duration_seconds and video_resolution in usage for cost tracking
End-to-end tests drive the real avideo_generation() entrypoint through an httpx
MockTransport and assert on the encoded request body, which is what catches a
mapper that drops input_reference before the request is built
Both model_prices maps gain minimax/MiniMax-H3 and minimax/MiniMax-H3-Max,
priced per output second by resolution tier from MiniMax's pay-as-you-go list
(H3 $0.08 at 768P and $0.13 at 2K, H3-Max $0.05 at 480P and $0.08 at 768P).
MiniMax also bills input images beyond a per-model free allowance (5 for H3, 2
for H3-Max), so each entry carries input_cost_per_image and
provider_specific_entry.minimax_free_input_images, and the create response
reports the full charge as provider_reported_cost_usd. Reference video input is
rejected with a 400, because MiniMax bills its duration only after the create
call that litellm bills
Co-authored-by: AaronHowell <237895480@qq.com>
* fix(minimax): fail closed on invalid video params and malformed task responses
Addresses review on #43705:
- A create, status or delete response without a task id (or a task) is a
502 instead of a queued object with an empty id
- An unmapped task status is a 502 instead of defaulting to queued
- seconds/duration must be a positive whole number and size a positive
WIDTHxHEIGHT or W:H, otherwise 400; a 0x0 size no longer divides by zero
and an unparseable duration no longer falls back to the billed default
- characters and image are rejected with UnsupportedParamsError unless
drop_params is set, instead of being dropped silently
- Downloading a task that is not ready or failed is a 400 that keeps its
message through the handler, instead of a ValueError surfaced as a
retried 500
- The video download goes through safe_get / async_safe_get
Co-authored-by: AaronHowell <237895480@qq.com>
* test(minimax): cover the video download path and invalid-param branches
The download now goes through safe_get, so the happy path and the
finished-task-without-a-url branch are asserted, alongside the non-object
parameters rejection and the remix response stub.
Generated with [Claude Code](https://claude.ai/code)
via [Happy](https://happy.engineering)
Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: Happy <yesreply@happy.engineering>
* fix(minimax): return 400 for video remix and clear new lint gate counts
* fix(minimax): send file-tuple images and keep input_reference next to content media
* fix(minimax): answer 404 when an H3 video is downloaded before it finishes
A download that lands while the task is still queued or running answered 400. That is a
client error, which was the intent, but 404 is what the OpenAI videos API documents for
content that is not ready, and it is the status the sibling DashScope and Volcengine
video paths now answer, so a client polling all three reads one shape
A failed task keeps its 400, since that is not a timing mistake the caller can retry
away. The download test drives avideo_content and asserts the caller sees NotFoundError
rather than an internal server error
* fix(minimax): answer 400 for unsupported video params, early downloads and unknown ids
A create param MiniMax cannot honor (seed, n, quality and the like) was dropped
without a word. It now answers 400 unless drop_params is on, like other providers
A download before the video is ready and a status call for an unknown task id
now answer 400. The router cools a deployment down on a 404 and retries a 5xx,
and neither is a deployment fault. DashScope on main answers 400 for the same case
Input images past the free allowance are now billed through the shared video cost
path, so a deployment with custom pricing bills them too. Status usage no longer
carries MiniMax's token counts, list sets has_more to false since MiniMax pages by
number, and the price map stops listing video as an input modality
* fix(minimax): keep the polled video id, honor deployment drop settings, and match chat's key order
Status re-encodes the id with the polled id's model when MiniMax omits it, drop_params and
additional_drop_params never reach MiniMax and listed keys are dropped, MINIMAX_API_KEY wins over
litellm.api_key, create sets created_at, and a content variant other than the video answers 400
* fix(minimax): list a task without a model under the routed group
MiniMax leaves `model` off some listed tasks. The caller polls the ids a list hands back, and the
proxy resolves a poll by the model in the id, so an id encoded with an empty model no longer routes
back to the deployment that served the task. Fall back to the group the router was asked for.
* fix(minimax): honor deployment drop keys inside parameters and bill images at the map rate when a deployment prices only seconds
A field the deployment lists in additional_drop_params no longer reaches MiniMax when it arrives inside parameters, and a deployment that sets only its per-second price now bills reference images past the free allowance at the cost map's per-image rate instead of $0
* fix(minimax): keep deployment-dropped fields out of the body when they arrive through extra_body
* fix(minimax): answer 400 for a video resolution with no price unless drop_params is on
* fix(minimax): send the default resolution when a deployment drops resolution
---------
Co-authored-by: AaronHowell <237895480@qq.com>
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: Happy <yesreply@happy.engineering>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(images): preserve explicit provider for async generation
* test(images): prove a bare model name reaches its explicit provider on the async path
* fix(images): validate async provider hints instead of reading untyped kwargs
Read custom_llm_provider and api_base in aimage_generation through a TypeAdapter over a small TypedDict, so the provider lookup gets typed values and the basedpyright gate stays within its merge-base ceiling
* fix: answer bad provider hints with a 400 and keep the explicit provider for async speech and transcription
* fix: keep the explicit provider for realtime and cover the audio bad-hint 400
* fix: keep the explicit provider on the realtime HTTP routes and read base_url in async audio
* fix: stop async image generation from rejecting a base_url it never reads
* fix(audio): skip base_url validation when api_base is set on async speech and transcription
* test(integration): cover a declared provider with a bare model on image, audio, and realtime routes
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(vertex_ai): return 400 with file line number for malformed batch JSONL row
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(vertex_ai): keep yield outside the JSON decode guard
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(http_handler): surface BaseLLMException raised while streaming a request body
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(http_handler): only follow __cause__ when unwrapping streamed body errors
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(http_handler): avoid recursive streamed error unwrapping
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(vertex_ai): return a 400 naming the line for wrong-shape and non-UTF-8 batch rows
Keep main's error path for a directly raised provider error and only unwrap
one found behind a transport error. Add the parser column to invalid JSON
errors, and cover the real aiohttp chain in the _handle_error test
* fix(vertex_ai): report every row conversion error as a 400 naming its line
A message with no role, a non-string role, a bad temperature, or a litellm
BadRequestError raised while converting a row still returned a 500, and the
async upload lost the 400 for litellm's own exceptions. The stream now turns
any error from one row's conversion into a 400 naming that line.
_handle_error only unwraps an httpx.TransportError, which is what the aiohttp
transport raises around a failing request body, so other callers keep their
own status. A file that starts with a UTF-8 byte order mark now uploads, and
provider messages start with 'Batch input file line N' like the proxy check.
* fix(vertex_ai): reject null or missing batch bodies and unparseable rows with a 400 naming the line
A chat row whose body is missing or not an object now raises a 400 naming
its line instead of uploading a blank prompt, and a non-object body on
line 1 no longer crashes the GCS object name lookup. Rows too deeply nested
or too long to parse return a 400 from both the SDK and the proxy's own
batch file check. JSON error columns now count leading whitespace.
* fix(vertex_ai): keep batch rows on their file line through router and guardrail rewrites
The Router model rewrite and batch guardrails now keep blank lines and blank out dropped records, so a provider error naming a line still points into the caller's file. The rewrite strips a leading BOM per line, and a non-string body.model on line 1 is reported as a 400 naming the line
* test(integration): cover Vertex batch row line errors across routes, rewrites, SDK and worker chaos
* fix(router): keep blank lines only for Vertex batch uploads and leave guardrail rewrites compact
* test(integration): score the whole OpenAI upload after a guardrail drop and cover a mixed OpenAI and Vertex batch upload
---------
Co-authored-by: jesus <jesus@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(llms): sanitize trailing slashes in provider get_complete_url
* test(llms): move trailing-slash url tests into each provider's mapped unit file
* fix(llms): strip trailing slashes in Perplexity search and Ollama embedding URLs and run the Topaz tests in CI
* fix(llms): strip trailing slashes in search, DashScope, VolcEngine, GigaChat and Gradient AI URL builders
* fix(fastcrw): strip a trailing slash from api_base and expect one slash in the GigaChat tests
* fix(gigachat): strip a trailing slash from the pass-through api_base
* test(integration): cover trailing-slash api_base across provider URL builders
Wire cells for chat, streaming, messages, responses, embeddings, search and pass-through with and without a trailing slash, an owned-proxy module for the flag-gated providers, env bases and a worker kill, two chaos bursts, and a Topaz SDK cell
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(vertex): report an unreadable credentials file as a read failure
Tell a path apart from inline JSON by shape, not os.path.exists().
Fixes#40166
* test(vertex): narrow pytest.raises to the expected message
* fix(vertex): log the credentials path instead of returning it to the caller
* refactor(vertex): log the credential source decision where it is made
* fix(vertex): handle paths open() rejects with a bare ValueError
* fix(vertex): redact the credentials path before it reaches the log
* test(vertex): cover the brace fallback and non-text credentials file
* test(vertex): exclude the unreachable exhaustiveness guard from coverage
* refactor(vertex): read the failure's fields off the matched object
CodeQL's py/uninitialized-local-variable does not follow keyword capture
patterns in a match statement and reported `path` and `reason` as possibly
unbound. Narrowing on the class and reading the attributes says the same thing
without a suppression.
* fix(vertex): accept a read-only mapping in extract_aws_params
* fix(vertex): keep encoded credentials out of the log and cover the legacy route
* fix(vertex): give one message for every credentials file failure and pick file or JSON by shape in Rust
* fix(vertex): read a `{`-named key file in Rust, name base64 there, and give one message when Google auth rejects a credentials file
* fix(vertex): read an unreadable `{`-named key file as a file, cap key file reads, and drop storage advice from the file message
* test(vertex): integration cells for credentials file and inline value failures
* test(vertex): drive the Rust messages cell through Vertex Claude
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(anthropic/adapter): stop translate_anthropic_tools_to_openai mutating the caller's input_schema
`translate_anthropic_tools_to_openai` assigned the caller's `input_schema`
straight into `function_chunk["parameters"]`, then merged the tool's remaining
top-level keys into that same dict. Because the assignment aliased rather than
copied, keys such as `display_width_px` on a computer tool were written back
into the caller's schema.
That matters because callers reuse the tool list across passes: the pass-through
guardrail translates the tools, and the request is then translated again for the
real call. The first pass pollutes the source and the second forwards a schema
carrying non-schema keys.
Shallow-copying fixes it. `translate_anthropic_output_format_to_openai` in the
same file already copies its schema for this reason, so this brings the two into
line.
Rebased onto current litellm_internal_staging; the original branch had gone to
conflict on surrounding drift while the line it fixes is unchanged.
* fix(anthropic/adapter): drop the inline comment and test the strict tool shape that Anthropic rejects
* fix(anthropic/adapter): deep copy input_schema so downstream schema edits never reach the caller's tool
A shallow copy still shared nested properties with the caller, and the Gemini, vLLM and watsonx bridges strip nested additionalProperties in place, so a fallback to Claude sent a strict tool Anthropic rejects. A deep copy also keeps a null or malformed input_schema as sent, as before this PR
* fix(anthropic/adapter): deep copy the incoming tools so downstream edits never reach the caller's tools
Copying only input_schema left the extra keys merged into parameters, such as
input_examples, and OpenAI-format function tools shared with the caller, so
Gemini's strict strip still edited the caller's tool before a fallback to Claude
* fix(anthropic/adapter): keep translating tools that deepcopy cannot copy
A tool passed as a frozen mapping, or with a schema nested past Python's recursion limit, made copy.deepcopy raise, so the request failed before it reached the provider. Copy each tool on its own and keep the original object when the copy fails, which is how these tools were handled before
* fix(anthropic/adapter): copy tools through JSON so the Responses bridge and frozen tools keep the caller's schema
* test(anthropic/adapter): parse stub bodies with json and cover the tool copy fallback
* test(anthropic/adapter): close the connection after the malformed tools request
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(proxy): scope /spend/tags aggregates to the caller's own and permitted-team logs
Non-admin callers previously received the proxy-wide per-tag spend
aggregation. The endpoint now resolves the same read scope as
/spend/logs/ui and /spend/logs/session/ui, and the query honors the
start_date/end_date parameters it previously accepted but ignored.
* fix(proxy): use Annotated auth dependency on /spend/tags and sync dashboard API types
* fix(proxy): bind /spend/tags dates as UTC datetimes, not session-timezone strings
Raw YYYY-MM-DD strings cast to timestamptz are read in the database
session timezone, shifting the day window by that offset. Parse them
to UTC-aware datetimes first, like every sibling spend query in this
file, and reject malformed dates with a 400 before they reach the
database.
* fix(auth): return a generic 401 for an unknown key
The wrong-key 401 shipped the internal verification table name and the
submitted key's full SHA-256 hash to any unauthenticated caller. Both now
go to the proxy's own log and the response carries only "Invalid API key
provided."
The Admin UI detected a dead session by string-matching that message, so it
now reads the structured error type instead.
* Update ui/litellm-dashboard/src/lib/http/client.ts
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* fix(auth): log the missing key hash where every resolve path passes
Review catch: JWT key-mapping misses resolve through IdentityStore without
touching the main key path, so logging the hash there left those callers with
nothing but the generic 401. Log it at the store's raise site instead, which
every resolve path shares, and drop the now-duplicate log.
* test(auth): inject a key-less client instead of patching the DB fetch
The store test tripped TQ008 by patching _fetch_key_object_from_db_with_reconnect.
Hand IdentityStore a client whose get_data returns no row instead, so the miss is
driven through the real lookup rather than around it.
* fix(auth): log the unknown key's hash once on the auth failure line
The per-lookup warnings in get_key_object and IdentityStore logged whatever value
they were handed, which on the failure-logging path is the raw key a caller sent
(a provider key included), let a newline in ?key= forge log lines, and fired on
lookups that expect a miss. The hash now rides KeyNotFoundError onto the single
existing auth failure log line next to the requester IP, with control characters
escaped. Also drops the new comments and updates skipped tests that quoted the
old message.
* test(auth): add the blank lines ruff format expects before _InFlightCountingPrisma
* test(integration): expect the generic wrong-key 401 in the management lifecycle tests
* fix(auth): keep the operator wording on the unknown-key log line and drop the redundant 401 rewrite
* test(auth): cover the generic unknown-key 401 across routes, proxies, and a worker kill
* test(auth): cover the realtime upgrade refusing an unknown key without echoing it
---------
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* test: inject clocks and wait on conditions in three wall-clock-flaky unit tests
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test: keep generic API logger tests on 3.10 and cover automatic sending
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test: pin router usage minute in shared rate-limit header test
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test: stabilize three wall-clock-sensitive unit tests
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(mcp): cap cold schema validation workers at the CPUs the proxy can use
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test: inject the cache clock and wait on lock contention instead of 1s deadlines
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(mcp): wait on listing events instead of 1s deadlines in the discovery drain test
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: mateo <mateo@berri.ai>
* fix(proxy): resolve model aliases to their target deployment for listing metadata
A model alias (a key's `aliases`, a team's `model_aliases`, or the router's
`model_group_alias`) is rewritten at request time and owns no deployment row,
so `get_model_listing_info` returns None for it and the alias name is not a
cost-map key either. Both metadata candidate sources come up empty and the
listing entry carries no `mode`, `max_input_tokens`, or `max_output_tokens`,
while the deployment it routes to reports them on the same response.
Resolve a listed alias to the deployment it routes to for the metadata lookup,
keeping the alias as the response id. Maps are applied in order, each against
the result of the last, mirroring how `litellm_pre_call_utils` applies the team
map and then the key map, so a chained alias reports the deployment the caller
actually reaches rather than an intermediate one. An alias shadowing a
same-named deployment follows the alias, because the request rewrite does too.
Fixes the model_aliases half of #40553.
* fix(proxy): look listed aliases up through the shared alias chain
Drop the separate alias resolver and retarget a listed alias in
alias_listing_entries and alias_target instead, so /v1/models and
/v1/models/{id} report the model /chat/completions rewrites it to, and
only when the caller can list that model
* fix(proxy): keep deployed listed models' own rows and resolve team alias targets at list and retrieval
* fix(proxy): resolve router_settings aliases, keep alias labels, and skip paused targets in model listing
* fix(proxy): leave an alias to an undiscoverable model bare in model listing
A team alias whose target has discoverable: false showed the hidden model's mode and limits on GET /v1/models and retrieved it by alias. Undiscoverable groups now count as hidden for alias resolution, as on main, while admins still see them.
* fix(proxy): leave an alias to a model a listing callback hides bare in model listing
* fix(proxy): keep aliases bare for models a listing callback never judged or another team owns
---------
Co-authored-by: haydster7 <haydster7@users.noreply.github.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
/public/providers/fields re-read and re-parsed the bundled 85K
provider_create_fields.json on every request, and /public/agents/fields did the
same for that file plus agent_create_fields.json. Both are unauthenticated
public endpoints, and both did the work inside an `async def`, so the read and
the parse ran on the event loop and serialized against every other in-flight
request. Measured against the bundled files, that is roughly 0.40 ms of blocking
work per request for the provider endpoint and 0.45 ms for the agent endpoint
Both now memoize with `lru_cache`, which is what `/public/endpoints` in the same
module already does through its `_cached_endpoints` global, so each file is read
once per process rather than once per request
The agent endpoint mutated the dicts it had just loaded, appending inherited
provider credential fields and popping `inherit_credentials_from_provider`. That
is only safe while the data is re-read every time, so caching it meant making the
merge pure: `_agent_with_inherited_credentials` builds a new mapping per agent,
and the cached value is the merged result rather than the raw file contents.
Responses are byte identical before and after, checked on both endpoints against
a live proxy
The bundled records are typed as Mapping[str, object] and narrowed with
isinstance at the point of use, rather than typing.Any, which ruff-strict bans
Side effects: 3 of the repo's ASYNC230 violations go away, and basedpyright
errors in the file drop from 63 to 52
Rebased onto main after /public/providers/fields started validating the bundled
JSON through a pydantic adapter on every request. That validation now runs once
and the validated records are cached; the agent endpoint keeps merging the raw
provider records, so both responses stay byte identical to main.
Co-authored-by: Alok Sharma <alok44170@gmail.com>
* test(integration): cache the two-turn guardrail replays and derive the thinking pass-through timeout
* test(integration): keep the thinking pass-through timeout as on main
The derived timeout had no failing run behind it, so this PR now only fixes the guardrail cache cells
Add live-captured cache-read fixtures and align replay usage blocks with current provider responses
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
The request-side sanitizer on /v1/messages dropped every thinking block whose
text is empty, even with a valid signature. Claude Opus 4.8 / Fable 5 / Fable 5.1
return exactly that shape by default (thinking.display omitted): the signature
is the preserved reasoning. Replaying the assistant turn as returned therefore
lost the model's reasoning on every multi-turn request, silently (the provider
answers 200 without it), and left preserved-thinking enforcement
(thinking.block_binding) nothing to bind.
Anthropic accepts an empty thinking block that carries a signature and binds it
to its prefix; only an empty block with no signature (the LIT-6357 case, a turn
a non-Anthropic model served through the bridge) is rejected with "each
thinking block must contain thinking". Use the signature-aware predicate
is_empty_unsigned_thinking_block, which #38809 already applied on the emit
side, for the request side too. The unsigned case is still dropped.
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(ui): status filter + sort and provider-aware search on Models & Endpoints
The models table mixes active and paused deployments with no way to tell
them apart beyond the pause toggle, and the drawer only filters by public
model name and access group.
Backend (/v2/model/info):
- new optional `blocked` query param to filter by routing status
- new `blocked` sort field (asc = active first); the existing `status`
field keeps sorting by config-vs-DB source
- `search` now also matches litellm_params.model, so typing a provider
or upstream model id (e.g. "nvidia_nim", "openai/gpt-4") finds
deployments whose public name doesn't mention them
Frontend (Models & Endpoints):
- visible Status column with Active/Paused badges, sortable
(active first on asc)
- Status filter (All/Active/Paused) in the Filters drawer, wired to the
new server param through URL state
The routing-status filter ignores non-bool sentinels the same way
exclude_auto_routers does, so direct calls bypassing FastAPI keep their
no-filter behavior.
* fix(ui): inverted routing-status filter mapping and compact Status column
Selecting "Active" in the Filters drawer sent blocked=true to the server,
showing the paused deployments instead of the active ones (and vice
versa). The mapping now lives in routingStatusToBlocked() with unit
tests, and the Status column is narrower so the row actions stay in
view.
* fix(ui): pin the Actions column to the right on the models table
With the new Status column the table overflows on narrower screens and
the pause/resume switches (last column) scroll out of view. Pinning the
Actions column keeps them visible; uses the existing DataTable
meta.pinned support (same pattern as MemberTable).
* refactor: readable db search where-condition and ruff-format clean
* fix: apply routing-status filter inside the search flow and regen API types
Greptile flagged (P1): with search + a status filter combined, the
bounded DB fetch ran before status filtering, so matches of the other
status could consume the page budget and leave short pages / stale
totals.
- extract _matches_routing_status() (sentinel-safe) and apply it to the
router-side matches inside _apply_search_filter_to_models
- push the status condition into the DB where clause of
_fetch_db_models_for_search, so rows of the other status never count
against the bounded fetch
- endpoint passes blocked through to the search flow; tests cover the
combined search+status case and the generated where clause
- regenerate ui/litellm-dashboard/src/lib/http/schema.d.ts for the new
query param
- update useModels.test.ts expectations for the new trailing argument
* fix(proxy): keep the flat DB where shape for single-condition searches
The upstream CI test
test_apply_search_filter_honours_exact_model_name_in_db_query pins the
Prisma where shape: exact model_name stays a top-level key, and plain
substring search keeps {model_name: {contains, mode}} at the top level.
Only genuinely multi-condition queries (status filter and/or router-id
exclusion) now wrap in AND.
* fix(proxy): avoid reassigning a Final variable in the db where builder
basedpyright's reportGeneralTypeIssues budget counts the reassignment of
the Final-annotated db_where_condition; express it as a single
conditional expression instead.
* chore(deps): bump pypdf to 6.19.0 to clear osv-scan
pypdf 6.16.2 has 8 High CVEs (GHSA-5jq2-8x83-x246 and aliases) fixed in
6.17.0-6.19.0; bump past all of them
* Revert "chore(deps): bump pypdf to 6.19.0 to clear osv-scan"
This reverts commit 0e2528123db7ab23304ae20f6a4c0961d0e2d073.
* fix(proxy): keep LIT002 budget flat in the model search where-builder
Extract the dynamic Prisma where-condition fragments into module-level
helpers so every dict starts on a short line, split the litellm_params
fallback lookup into a Final local, and annotate suppressions on the
construct start lines the checker attributes
* chore(tests): repoint unbounded-IN baseline at the extracted where-builder helper
The bounded-router-id filter moved from _fetch_db_models_for_search into
_not_in_router_ids, so the old baseline key is stale and the new helper
site is the finding to record
* fix(proxy): avoid searching encrypted deployment model names
* refactor(ui): keep model routing status as a filter only
* docs(api): regenerate model info sorting description
* fix(api): remove remaining paused status sort references
---------
Co-authored-by: thementalcoding <312162298+thementalcoding@users.noreply.github.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
LiteLLM_AuditLogs masked before_value and updated_values with
SensitiveDataMasker.mask_dict, which stringifies every non-native leaf, so a
null in the change was stored as the string "None" (or "" under a key-named
field). Walk audit values with mask_credentials_in_payload instead, injecting
the existing {"key"} masker so the masked format and scope stay the same.
* fix(proxy): gzip responses over 16 KiB off the event loop
GZipBufferedResponseMiddleware compressed every body under 1 MiB inline.
gzip.compress holds the event loop for its whole run, and its cost follows
how well the body compresses: an 824 KB base64 embeddings response (100
inputs, the OpenAI SDK default encoding) takes about 165 ms, so every other
request on the worker waits that long, and with several in flight the
compressions run back to back.
Lower OFF_LOOP_SIZE_BYTES to 16 KiB so only bodies that compress in well
under a millisecond stay on the loop. zlib releases the GIL, so the loop
keeps serving while a worker thread compresses.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* test(proxy): pin the gzip off-loop boundary at exactly OFF_LOOP_SIZE_BYTES
A body of exactly OFF_LOOP_SIZE_BYTES compresses in a worker thread, as the
`<` in _compress implies. Suggested in review.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* test(proxy): pin an embeddings-sized gzip body off the event loop and drop the source comment
The off-loop test was relative to OFF_LOOP_SIZE_BYTES, so it still passed with the old
1 MiB line. An 824 KB body, the size of the 100-input base64 embeddings response in the
issue, now has to compress in a worker thread. The recorder no longer mutates a list, and
the threshold rationale stays in the commit history instead of a source comment.
---------
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* feat(typesafe): support Jev 1.14 images, box questions, and long-context headers
* fix(cost): drop the typesafe/jev-1.14.0 entry main already prices
* fix(typesafe): send images only with the images header and box questions only to models that answer them
TypeSafe takes assets and input_image parts only when the request carries
TypeSafe-Danger-Zone-Images: true, so without it they follow drop_params like
any other provider that cannot take images. Box support now comes from a
supports_box_questions cost-map flag set on jev-1.14.0 and jev-preview, so
jev-1.13.0 and jev-latest refuse or drop a box question before the call. A
malformed provider_specific_header answers 400 instead of 500.
* fix(typesafe): allow null box confidence and validate asset references and base64
* fix(typesafe): gate box questions on the images header, drop unreadable assets under drop_params, forward opt-in headers on the pass-through
---------
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix: index streamed thinking_blocks so the OpenAI SDK .stream() accumulator accepts them
* fix: satisfy the type discipline and basedpyright gates for the thinking block index
* chore: sync the OpenAPI snapshot and schema.d.ts with the thinking block index field
---------
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
LiteLLM_AuditLogs parses before_value and updated_values as JSON but only re-serialized dicts, so a list change reached Prisma as a Python list and the row write failed. Every deployment warning ignore, snooze and restore hit this. Mask and re-serialize any JSON value instead
* fix(responses): translate OpenAI tool_search to Anthropic tool search for Claude models
* fix(responses): define the tool_search_call output item locally for older openai SDKs
* fix(responses): gate tool search on the Anthropic request builder and validate deferred tool fields
Bedrock Converse builds its own request and has no tool search, so the bridge now drops the tool_search tool there with a warning instead of sending a tool the transport loses, while Bedrock invoke, Anthropic, and Vertex keep it. A replayed tool_search_call with no stored result is dropped from the Anthropic request so a previous_response_id follow-up no longer answers 400. Only srvtoolu_ ids whose name is one of Anthropic's search tools count as search calls, so a caller function named like one stays a function call. A non-boolean defer_loading or a non-list allowed_callers now answers 400 instead of 500
* fix(responses): keep an answered search call on replay and send tools undeferred when no search tool goes out
A replayed Claude tool search call that the caller answered with a tool message kept its
tool_result in the user turn while its tool_use was dropped from the assistant turn, so
Anthropic answered 400 on the OpenAI-SDK tool loop. The search call is now dropped only when
no tool message answers it.
The Responses bridge now strips defer_loading from every tool whenever no tool search tool is
sent, whether the caller sent none or the route cannot run it, since a deferred tool with no
search tool can never be loaded. Namespace members had started carrying the flag to every
bridge provider, which providers that reject unknown tool keys answered 422.
The audit round-1 integration cells for the bridge wire and chaos paths ride in this commit.
* fix(responses): gate only the OpenAI tool_search shape and keep native Anthropic search tools intact
A native tool_search_tool_regex_20251119 or tool_search_tool_bm25_20251119 tool the caller sends through /v1/responses passes through the bridge on every route as before, and the other tools keep defer_loading whenever any Anthropic search tool goes out. The route gate now drops the OpenAI tool_search item before translation instead of filtering the translated regex tool, so bm25 counts as a search tool. answered_tool_call_ids walks the history with islice instead of a slice copy. The tool_search_call output item drops its created_by field, which OpenAI rejects on a replayed input item and the streaming item never carried
* fix(responses): drop malformed tool flags under drop_params and keep stored search calls off non-Anthropic follow-ups
With drop_params on, a non-boolean defer_loading or a non-list allowed_callers on a Responses API tool (or a namespace member) is dropped before the bridge instead of answering 400, so only a request without drop_params gets the 400. A custom tool now carries defer_loading to the chat tool like a function tool does
A previous_response_id follow-up routed to a provider that does not build an Anthropic request no longer replays the stored Claude search call (srvtoolu_ id) as a client tool call. Mistral answered that history 400 "Unexpected tool call id". An assistant turn that held only the search call is dropped there. The Anthropic route keeps the call, since its factory pairs it with the stored result
The tool_search_call output model is renamed OutputToolSearchCall so the proxy's OpenAPI schema keeps the OpenAI SDK's ResponseToolSearchCall name. answered_tool_call_ids reads the history by index, so the lookup is linear in the history length. The streaming done event reads the response it is given, which is the same object it used to read from the iterator
* fix(responses): validate function allowed_callers, send one search tool, and keep replays clean off Anthropic
A function tool whose allowed_callers is not a list of strings now answers 400
instead of 500. The guardrail scan reads tools without malformed flags, so the
route decides between a 400 and a drop under drop_params. When the caller sends
a native Anthropic search tool beside tool_search, only the native one goes out.
Databricks Claude no longer counts as an Anthropic request route.
A previous_response_id follow-up off Anthropic also drops a search call the
client replayed as a function_call with a srvtoolu_ id, and its output. The
answered tool id scan in the Anthropic message factory runs once per assistant
run instead of once per message. A null created_by on tool_search_call and
tool_search_output output items is left out of the serialized response, so a
cache hit returns the same keys as the first call.
* fix(guardrails): keep OpenAI tool_search out of the Responses guardrail tool scan
The scan translated tool_search into Anthropic's regex search tool on every
route, so a guardrail returning only function tools removed tool_search from
native OpenAI requests and OpenAI answered 400 for the deferred tools
* fix(anthropic): drop answered tool search replays beside client calls, place system before dropped search turns, flag tool search on azure_ai
* fix(anthropic): keep answered tool search replays paired with their answers
* fix(anthropic): place mid-conversation system messages in one pass and scan tool_search_call in output guardrails
* fix(guardrails): send empty incomplete tool_search_call arguments when a rewrite is not a JSON object
* fix(responses): keep the caller's defer_loading on Claude routes when no server tool_search is sent
* fix(guardrails): deliver streamed rewrites when OpenAI's tool_search_call has no call_id
* fix(responses): answer 400 to a tool_search execution that is not server or client
OpenAI's own Responses API answers 400 invalid_value to it. With drop_params the malformed
execution is dropped like the other malformed tool flags, which leaves a server search
* test(responses): cover the 400 and drop_params paths for a malformed tool_search execution
* fix(responses): map Anthropic tool search to OpenAI server tool search
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses): sync generated tool search API types
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses): type tool search mapping predicates
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses): validate tool-search mappings
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses): use the openai SDK ToolSearchToolParam for the mapped tool search
* fix(responses): keep deferred chat tools beside a tool search on the responses bridge
Restore the hand-rolled OpenAIToolSearchToolParam, since openai 2.20.0 (the
supported floor) has no ToolSearchToolParam, and carry defer_loading from chat
function tools onto the Responses request when a tool search tool is present,
which OpenAI requires for tool_search
* fix(responses): leave the bridge's defer_loading carry to its own PR
#46439 owns keeping defer_loading on chat function tools sent through the
Responses bridge, so this PR keeps only the tool search mapping and the
openai 2.20.0 compatible tool type
* fix(openai): keep tool search mapping to the OpenAI API and drop it where the model or history cannot take it
* fix(openai): narrow the tool search drop to Anthropic's search tool, drop it when nothing is deferred, and run the wire tests against the OpenAI host
* fix(openai): pass malformed tools to OpenAI as sent and keep a caller's own tool_search under drop_params
* perf(openai): scan Responses history for Claude search calls only when Anthropic's search tool is sent
* test(responses): cover the Anthropic tool search mapping to OpenAI Responses across routes, settings, and chaos
* test(integration): define the fallback cell's deployments in the gateway config so both workers load them
* test(integration): keep the worker-kill burst held past the replacement wait
* fix(responses bridge): count an Anthropic search tool as hosted tool search on the chat bridge
main's #46439 refuses defer_loading when no tool_search tool is in the request, and the chat bridge only counted {"type": "tool_search"}, so the Anthropic bm25 or regex tool this PR maps to OpenAI's tool search was refused before the mapping ran
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(integration): expect the chat bridge to map the search tool now that defer_loading survives
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(xai): report a completed xAI batch's results as its output file details
* fix(xai): keep a short results download and per-call timeouts out of the reported file details
* fix(xai): describe a batch's results file from the batch alone and let the batch job fill its size
Reading an xAI batch id as a file now costs one batch read and never downloads
the results: the file reports a 0 byte placeholder, the results filename and
the registration time. The managed proxy fills in the size the batch cost job
measures when it downloads the results, keeps a known size when a provider
reports none, and drops the per-process results download cache.
* fix(managed_files): keep a measured batch output size when a refresh races the batch job
A refresh that read the row before the batch cost job stored the measured size
could save the provider's unsized details over it. The refresh now reads the
row back from the writer and saves only while the row still holds what it read,
retrying when another write landed in between. The new tests drive the real
xAI and OpenAI retrieve paths through a fake HTTP upstream.
* fix(managed_files): guard the refresh save with the row the request already read
The conditional save first compares against the row the request read, so a
refresh adds no database read unless another write landed in between; only
then does it read the row back from the writer and try again.
* fix(managed_files): save a refreshed file object with one guarded write and no extra database reads
A refresh that loses its guarded write to a newer one now leaves that row
alone and drops this worker's cached copy, so the next read loads the newer
row instead of retrying against the database on the request path
* fix(managed_files): retry the batch cost job's measured size after a read refreshes the row first
The batch cost job re-reads the row from the writer and saves again when its
guarded write loses to a concurrent refresh, so its measured size is never
dropped. Reads without a measured size still make one write and no re-read
* fix(managed_files): size a concurrently refreshed row instead of restoring the batch job's placeholder copy
When the batch cost job loses its guarded write and the row it re-reads holds
refreshed details, it applies its measured size to those details, so the
results filename and any size the provider reported are kept
* fix(managed_files): keep a batch output's earlier created_at when a refresh replaces its placeholder
A refresh saved over a stored row now keeps the earlier of the two creation
times, so an xAI placeholder keeps the time the proxy registered it instead of
the read time, while a provider's own earlier creation time still wins
* test(integration): an xAI placeholder row keeps its registration time after the proxy refreshes it
* test(integration): drop the cached output row so the placeholder cell reads the seeded database row
* test(integration): drop the cached output row without requiring it to still exist
* fix(managed_files): limit the batch output size fill, guarded save and kept created_at to xAI rows
* fix(xai): keep the batch id as the batch output filename
Managed xAI batch output files keep the filename main already returns
(the batch id) instead of renaming them to <batch id>_results.jsonl.
---------
Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com>
* fix(responses): translate OpenAI tool_search to Anthropic tool search for Claude models
* fix(responses): define the tool_search_call output item locally for older openai SDKs
* fix(responses): gate tool search on the Anthropic request builder and validate deferred tool fields
Bedrock Converse builds its own request and has no tool search, so the bridge now drops the tool_search tool there with a warning instead of sending a tool the transport loses, while Bedrock invoke, Anthropic, and Vertex keep it. A replayed tool_search_call with no stored result is dropped from the Anthropic request so a previous_response_id follow-up no longer answers 400. Only srvtoolu_ ids whose name is one of Anthropic's search tools count as search calls, so a caller function named like one stays a function call. A non-boolean defer_loading or a non-list allowed_callers now answers 400 instead of 500
* fix(responses): keep an answered search call on replay and send tools undeferred when no search tool goes out
A replayed Claude tool search call that the caller answered with a tool message kept its
tool_result in the user turn while its tool_use was dropped from the assistant turn, so
Anthropic answered 400 on the OpenAI-SDK tool loop. The search call is now dropped only when
no tool message answers it.
The Responses bridge now strips defer_loading from every tool whenever no tool search tool is
sent, whether the caller sent none or the route cannot run it, since a deferred tool with no
search tool can never be loaded. Namespace members had started carrying the flag to every
bridge provider, which providers that reject unknown tool keys answered 422.
The audit round-1 integration cells for the bridge wire and chaos paths ride in this commit.
* fix(responses): gate only the OpenAI tool_search shape and keep native Anthropic search tools intact
A native tool_search_tool_regex_20251119 or tool_search_tool_bm25_20251119 tool the caller sends through /v1/responses passes through the bridge on every route as before, and the other tools keep defer_loading whenever any Anthropic search tool goes out. The route gate now drops the OpenAI tool_search item before translation instead of filtering the translated regex tool, so bm25 counts as a search tool. answered_tool_call_ids walks the history with islice instead of a slice copy. The tool_search_call output item drops its created_by field, which OpenAI rejects on a replayed input item and the streaming item never carried
* fix(anthropic): translate the OpenAI tool_search tool on /v1/chat/completions for Claude models
* fix(responses): drop malformed tool flags under drop_params and keep stored search calls off non-Anthropic follow-ups
With drop_params on, a non-boolean defer_loading or a non-list allowed_callers on a Responses API tool (or a namespace member) is dropped before the bridge instead of answering 400, so only a request without drop_params gets the 400. A custom tool now carries defer_loading to the chat tool like a function tool does
A previous_response_id follow-up routed to a provider that does not build an Anthropic request no longer replays the stored Claude search call (srvtoolu_ id) as a client tool call. Mistral answered that history 400 "Unexpected tool call id". An assistant turn that held only the search call is dropped there. The Anthropic route keeps the call, since its factory pairs it with the stored result
The tool_search_call output model is renamed OutputToolSearchCall so the proxy's OpenAPI schema keeps the OpenAI SDK's ResponseToolSearchCall name. answered_tool_call_ids reads the history by index, so the lookup is linear in the history length. The streaming done event reads the response it is given, which is the same object it used to read from the iterator
* fix(bedrock): answer 400 for the OpenAI tool_search tool on Converse unless drop_params is set
* fix(responses): validate function allowed_callers, send one search tool, and keep replays clean off Anthropic
A function tool whose allowed_callers is not a list of strings now answers 400
instead of 500. The guardrail scan reads tools without malformed flags, so the
route decides between a 400 and a drop under drop_params. When the caller sends
a native Anthropic search tool beside tool_search, only the native one goes out.
Databricks Claude no longer counts as an Anthropic request route.
A previous_response_id follow-up off Anthropic also drops a search call the
client replayed as a function_call with a srvtoolu_ id, and its output. The
answered tool id scan in the Anthropic message factory runs once per assistant
run instead of once per message. A null created_by on tool_search_call and
tool_search_output output items is left out of the serialized response, so a
cache hit returns the same keys as the first call.
* fix(anthropic): send one search tool and judge tool search on the real model for chat tool_search
Several OpenAI tool_search items now collapse into one tool_search_tool_regex
tool, since Anthropic answers 400 when two tools share a name. A native
Anthropic search tool the caller already sent wins over the OpenAI item.
Bedrock invoke and Vertex AI now decide tool search support on the real model
before tool-based response_format swaps in a stub model name, so a Claude 5.5
request with response_format no longer gets a 400 on a route that runs tool
search.
* fix(anthropic): name the proxy drop_params setting in the tool_search 400
* fix(guardrails): keep OpenAI tool_search out of the Responses guardrail tool scan
The scan translated tool_search into Anthropic's regex search tool on every
route, so a guardrail returning only function tools removed tool_search from
native OpenAI requests and OpenAI answered 400 for the deferred tools
* fix(anthropic): run tool search on azure_ai, gate it on Databricks Claude, and strip deferral without recursion
Anthropic's Foundry docs list no tool search exclusion for either hosting
option, so the claude-tool-search rule now fills azure_ai. Databricks Claude
models route their tools through the same unsupported-tool-search gate, so a
tool_search tool answers 400 or drops under drop_params instead of a 500.
_without_deferral strips two fixed levels instead of walking recursively.
* fix(anthropic): drop answered tool search replays beside client calls, place system before dropped search turns, flag tool search on azure_ai
* fix(anthropic): keep answered tool search replays paired with their answers
* fix(anthropic): drop Claude tool search calls from history sent to other routes, name the fix in the 400
A second turn that carries Claude's srvtoolu_ search call and its result now has both removed
before completion() sends it to a route that does not build an Anthropic request, including the
responses bridge. The tool search 400 now names model_info supports_tool_search for a model the
cost map does not know, and bedrock/invoke for a Converse model that runs tool search there.
* fix(anthropic): place mid-conversation system messages in one pass and scan tool_search_call in output guardrails
* fix(chat): keep answered Claude tool search calls in history sent to other providers
Only a srvtoolu_ search call with no tool result is dropped before a
non-Anthropic route, so an answered pair keeps the caller's shape where
OpenAI and Gemini take it. The history check no longer copies the list
through a TypeAdapter.
* fix(guardrails): send empty incomplete tool_search_call arguments when a rewrite is not a JSON object
* fix(chat): drop unanswered Claude tool search calls only on routes that reject them
OpenAI, Azure OpenAI and Bedrock Converse answer 400 for a srvtoolu_ call
with no tool result, while Gemini answers 200. Keep the history as sent
everywhere else so routes that already work see what the caller sent.
* fix(responses): keep the caller's defer_loading on Claude routes when no server tool_search is sent
* test(integration): cover chat tool_search routing, history drop and load on the wire
Audit round 1 of 3 at 9150723c84: 58 cells across the Claude routes, Converse, Databricks, the drop_params sources, replayed history on openai, azure, Converse and gemini, and a burst, an outage and a worker kill
* fix(guardrails): deliver streamed rewrites when OpenAI's tool_search_call has no call_id
* fix(responses): answer 400 to a tool_search execution that is not server or client
OpenAI's own Responses API answers 400 invalid_value to it. With drop_params the malformed
execution is dropped like the other malformed tool flags, which leaves a server search
* test(responses): cover the 400 and drop_params paths for a malformed tool_search execution
* test(responses): cover a Claude search call replayed to Bedrock Converse with cache marks
* fix(anthropic): map only a server or unset tool_search execution to Anthropic tool search, so a malformed value answers 400
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(anthropic): judge the chat tool_search gate with a strict server check and keep the Responses bridge classification
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style(responses): sort the tool_search __all__
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses bridge): keep defer_loading on function tools sent to the Responses API
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses bridge): give the defer_loading casts a cast-ok reason
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(responses bridge): build defer_loading outside the tool loop so Final stays valid
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses bridge): round-trip tool_search_call and tool_search_output items between turns
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(responses bridge): read replayed tool-search and namespace fields through typed helpers
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses bridge): keep tool_search items on a turn with no message or tool call
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses bridge): keep every reasoning item on a search-only turn
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses bridge): leave content unset on a search-only turn so replay adds no empty message
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses bridge): replay tool search items only with tool_search, count replayed tools in the allowlist, keep namespace off guardrails
* fix(responses bridge): honor drop_params for defer_loading and route tool_search to Responses
Under drop_params, defer_loading is dropped when no tool_search tool is sent,
so the 400 it would cause upstream goes away as before. A non-bool value answers
400 without drop_params and is dropped with it. A null tool-level value falls
back to the nested function value. A request carrying a tool_search tool now
bridges to Responses on OpenAI and Azure endpoints even with reasoning_effort
none, since chat completions rejects the tool_search type.
* fix(responses bridge): replay tool search items in output order and count replayed tools in tool policy
Store the ordered output prefix through the last tool search item, reasoning included, so a
second reasoning item no longer makes OpenAI reject the replay. The shared chat-to-Responses
converter now replays tool search items and namespace only when the bridge opts in, so Noma,
the complexity router and guardrail payloads keep their shape. The tool policy guardrail and
the allowlist count replayed tools only when the request can send them.
* fix(responses bridge): forward defer_loading only beside a tool_search tool
defer_loading only means something with tool_search, and backends without it
reject the key (xAI answers 403). A true value with no tool_search tool now
answers 400 naming drop_params and is dropped under drop_params; a false value
there is a no-op and is dropped.
* fix(tool_loop): keep tool search items and namespace between rounds
* fix(responses bridge): check replayed tool search loads when a deployment adds tool_search
* fix(chat): drop litellm response fields before strict OpenAI-compatible providers
A chat reply from the Responses bridge carries provider_specific_fields on the
message and on each tool call, plus reasoning_items. Groq, Fireworks, Mistral
and Together reject unknown fields, so sending that reply back to them as
history failed with a 400 or 422. Strip them in the shared helper and use it
in those providers.
* fix(caching): keep reasoning_items and tool search items on response cache hits
A cached bridge response lost reasoning_items on a non-streaming hit and lost
both reasoning_items and provider_specific_fields (tool_search_items) on a
streamed hit, so the next turn could not replay the search or the reasoning
around it
* test(integration): cover tool search item replay on the chat to Responses bridge
Proxy, SDK, authorization and chaos cells for returning and replaying
tool_search_call and tool_search_output items and the tool call namespace
* test(integration): add response cache twins for the tool search guardrail cells
Each guardrail cell now has a twin where the answer is already in the Redis response cache: a key opted out of global guardrails caches a replay that loads a policy-blocked tool, the same replay without guardrails is cached before block mode sees it, rewrite mode is not served the unrewritten conversation's cached answer, and an allowed replay is served from the cache.
* fix(responses bridge): forward defer_loading beside a client-executed tool_search
The bridge now replays a client tool_search_call and the client's tool_search_output through provider_specific_fields.tool_search_items, so a deferred function beside a client tool_search keeps defer_loading like it does beside a hosted one
* test(responses bridge): pin the client tool_search turn and its replay, streamed and not
* fix(responses bridge): strip nested namespace for strict providers and keep streamed reasoning_items on text replies
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses bridge): keep dropping defer_loading beside a client-executed tool_search
Forwarding defer_loading beside a client tool_search made GPT stop with a
search call the chat client has to answer by hand, so a plain question that
used to get tool calls got none. Keep main's behavior of dropping it there.
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor: read typed credential fields directly in per-user Copilot auth
- github_copilot_auth_mode reads credential.credential_values directly instead of getattr+isinstance+cast
- _new_async_github_client uses plain attribute access for get_async_httpx_client
- _per_user_oauth_configured and _all_per_user_credential_names iterate typed CredentialItem fields
- list_user_connections builds login/connected_at maps in one shot instead of seeded mutable dicts
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor: read typed fields directly in team member budget auth and pass-through route registration
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* Revert "refactor: read typed fields directly in team member budget auth and pass-through route registration"
This reverts commit ae37be318d.
* refactor: deduplicate Ternary export locking and type HTTP handler override
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor: drop dead telemetry helpers, reuse PrismaClient.writer_db, remove restating comments
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* ci: retrigger unit shards after an unrelated mcp-elicitation timeout
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Providers like Mistral put every parallel tool call, with full arguments, into one
chat-completions chunk. The /v1/messages adapter opened one tool_use block for the
first call and concatenated every call's arguments into it, so clients got invalid
JSON like {...}{...}. _CombinedChunkSplitter now splits such a chunk into one chunk
per call. The first call streams live and calls opened behind it are held, gathering
their fragments by index, until a non-fragment chunk arrives or the stream ends
Fixes#44029
* fix(router): keep the prompt caching affinity pin alive as long as the prefix's cache_control ttl
A prefix marked cache_control ttl 1h lost its pinned deployment after the fixed 300 s, so the next request went to another deployment and paid a cold cache. The pin TTL now follows the longest ephemeral breakpoint in the cacheable prefix, tools included: 3600 s for a 1h breakpoint and 300 s otherwise.
* fix(router): give prompt cache pins their own tier and honour any declared ttl
Pins now live in a DualCache of their own instead of the router's 200-slot
in-memory tier, so an hour-long pin no longer evicts cooldowns and rate windows.
The pin lifetime follows any <n>s|m|h cache_control ttl, floored at 300 s, not
only the literal "1h".
* fix(router): hash the pin ttl off the event loop and flush prompt cache pins with the router cache
Compute the cache_control ttl scan through offload_token_count beside the
prefix hash, clear the dedicated pin cache in Router.flush_cache, and point
the order-fallback and priority-group pin tests at the pin cache they read
* fix(router): pin 1h prefixes only on 1h-capable models, cap the pin at 1h, and give pins 10k in-memory slots
* fix(router): match the 1h pin gate to what Bedrock forwards
Bedrock deployments now decide the 1h pin with is_claude_4_5_on_bedrock,
the same check that decides whether the 1h ttl reaches Bedrock, so an
application inference profile ARN keeps a 5 minute pin whatever its
base_model prices
* test(router): give the anthropic_messages pin fixture its provider
* fix(router): let each pin write set its own lifetime and require a positive 1h price
In a group mixing 1h and 5m deployments the in-memory pin kept the lifetime of its first write, so a 5m deployment could hold an hour-long pin. Each write now clears the in-memory entry first, matching Redis. The Bedrock gate uses supports_bedrock_prompt_cache_ttl after #46058, and a zero 1h price no longer counts as 1h support.
* test(router): integration cells for the one-hour prompt cache pin lifetime
Cover every surface the pin lifetime touches: chat and /v1/messages through
both SDKs, the responses bridge, response-cache hits, each provider family's
1h gate, malformed and duplicated ttl values, rewrites of one prefix, mixed
and cross-group routing, concurrency, every Redis attach path, the in-memory
slot pressure without Redis reads, a Redis outage and a killed worker
* fix(caching): bound the in-memory expiry heap and count prompt cache pins in the memory debug routes
* test(integration): cover prompt cache pins in the memory debug routes and the bounded expiration heap
* fix(caching): serialize in-memory cache writes so a heap rebuild cannot lose another thread's expiry entry
* fix(caching): keep the in-memory cache bounded when entries have no usable expiry
* test(caching): cover the response cache bound when cached entries have no usable ttl
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* test(integration): follow #44788 and #46101 in the openrouter translation and cache mark census expectations
Expect prompt_cache_breakpoint for opted-in OpenAI and omit assistant tool-call marks from the capped census
Match OpenRouter refusal, logprobs, and system fingerprint nulls
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(integration): follow #46341 blank env and guardrail param handling in keepalive and scope expectations
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(router): read the first stream chunk through the stream's iterator so MCP streams keep working
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(integration): follow #46493 retry and fallback semantics for streams that fail before content
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(integration): drop the openrouter expectations #46510 already landed on main
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(integration): restore blank lines lost in the main merge
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses): translate OpenAI tool_search to Anthropic tool search for Claude models
* fix(responses): define the tool_search_call output item locally for older openai SDKs
* fix(responses): gate tool search on the Anthropic request builder and validate deferred tool fields
Bedrock Converse builds its own request and has no tool search, so the bridge now drops the tool_search tool there with a warning instead of sending a tool the transport loses, while Bedrock invoke, Anthropic, and Vertex keep it. A replayed tool_search_call with no stored result is dropped from the Anthropic request so a previous_response_id follow-up no longer answers 400. Only srvtoolu_ ids whose name is one of Anthropic's search tools count as search calls, so a caller function named like one stays a function call. A non-boolean defer_loading or a non-list allowed_callers now answers 400 instead of 500
* fix(responses): keep an answered search call on replay and send tools undeferred when no search tool goes out
A replayed Claude tool search call that the caller answered with a tool message kept its
tool_result in the user turn while its tool_use was dropped from the assistant turn, so
Anthropic answered 400 on the OpenAI-SDK tool loop. The search call is now dropped only when
no tool message answers it.
The Responses bridge now strips defer_loading from every tool whenever no tool search tool is
sent, whether the caller sent none or the route cannot run it, since a deferred tool with no
search tool can never be loaded. Namespace members had started carrying the flag to every
bridge provider, which providers that reject unknown tool keys answered 422.
The audit round-1 integration cells for the bridge wire and chaos paths ride in this commit.
* fix(responses): gate only the OpenAI tool_search shape and keep native Anthropic search tools intact
A native tool_search_tool_regex_20251119 or tool_search_tool_bm25_20251119 tool the caller sends through /v1/responses passes through the bridge on every route as before, and the other tools keep defer_loading whenever any Anthropic search tool goes out. The route gate now drops the OpenAI tool_search item before translation instead of filtering the translated regex tool, so bm25 counts as a search tool. answered_tool_call_ids walks the history with islice instead of a slice copy. The tool_search_call output item drops its created_by field, which OpenAI rejects on a replayed input item and the streaming item never carried
* fix(responses): drop malformed tool flags under drop_params and keep stored search calls off non-Anthropic follow-ups
With drop_params on, a non-boolean defer_loading or a non-list allowed_callers on a Responses API tool (or a namespace member) is dropped before the bridge instead of answering 400, so only a request without drop_params gets the 400. A custom tool now carries defer_loading to the chat tool like a function tool does
A previous_response_id follow-up routed to a provider that does not build an Anthropic request no longer replays the stored Claude search call (srvtoolu_ id) as a client tool call. Mistral answered that history 400 "Unexpected tool call id". An assistant turn that held only the search call is dropped there. The Anthropic route keeps the call, since its factory pairs it with the stored result
The tool_search_call output model is renamed OutputToolSearchCall so the proxy's OpenAPI schema keeps the OpenAI SDK's ResponseToolSearchCall name. answered_tool_call_ids reads the history by index, so the lookup is linear in the history length. The streaming done event reads the response it is given, which is the same object it used to read from the iterator
* fix(responses): validate function allowed_callers, send one search tool, and keep replays clean off Anthropic
A function tool whose allowed_callers is not a list of strings now answers 400
instead of 500. The guardrail scan reads tools without malformed flags, so the
route decides between a 400 and a drop under drop_params. When the caller sends
a native Anthropic search tool beside tool_search, only the native one goes out.
Databricks Claude no longer counts as an Anthropic request route.
A previous_response_id follow-up off Anthropic also drops a search call the
client replayed as a function_call with a srvtoolu_ id, and its output. The
answered tool id scan in the Anthropic message factory runs once per assistant
run instead of once per message. A null created_by on tool_search_call and
tool_search_output output items is left out of the serialized response, so a
cache hit returns the same keys as the first call.
* fix(guardrails): keep OpenAI tool_search out of the Responses guardrail tool scan
The scan translated tool_search into Anthropic's regex search tool on every
route, so a guardrail returning only function tools removed tool_search from
native OpenAI requests and OpenAI answered 400 for the deferred tools
* fix(anthropic): drop answered tool search replays beside client calls, place system before dropped search turns, flag tool search on azure_ai
* fix(anthropic): keep answered tool search replays paired with their answers
* fix(anthropic): place mid-conversation system messages in one pass and scan tool_search_call in output guardrails
* fix(guardrails): send empty incomplete tool_search_call arguments when a rewrite is not a JSON object
* fix(responses): keep the caller's defer_loading on Claude routes when no server tool_search is sent
* fix(guardrails): deliver streamed rewrites when OpenAI's tool_search_call has no call_id
* fix(responses): answer 400 to a tool_search execution that is not server or client
OpenAI's own Responses API answers 400 invalid_value to it. With drop_params the malformed
execution is dropped like the other malformed tool flags, which leaves a server search
* test(responses): cover the 400 and drop_params paths for a malformed tool_search execution
* test(responses): cover a Claude search call replayed to Bedrock Converse with cache marks
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: mateo <mateo@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(cost): price typesafe/jev-1.14.0
TypeSafe released jev-1.14.0 on 2026-10-14 at the same price as jev-1.13.0
($0.042 per 1M input tokens, output free; https://docs.typesafe.ai/models).
Without an entry, calls through the TypeSafe pass-through log a cost of 0:
TypeSafePassthroughLoggingHandler prices from litellm.model_cost["typesafe/<model>"],
and a deployment's model_info price is registered under its model id only.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* fix(cost): add max_input_tokens to the typesafe jev rows
---------
Co-authored-by: Mike Hyams <241361536+hyamie@users.noreply.github.com>
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(integration): add rich anthropic claude haiku 5.5, opus 5.5 and fable 5.1 translation cases
Capture-backed basic cases on /v1/chat/completions, /v1/messages and /v1/responses for claude-haiku-5-5, claude-opus-5-5 and claude-fable-5-1, reusing the shared client templates and ANTHROPIC_MESSAGES_REQUEST/RESPONSE with model-specific id, content and usage overrides. The minimal Opus 5.5 hello cases are replaced by the rich conversation. Sonnet 5.5 and legacy models are unchanged
* test(translation): rich OpenAI captures for gpt-6.1-sol, gpt-6-luna, gpt-6-astra and gpt-5.6-terra
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(translation): rich Azure gpt-6.1-sol, gpt-6-luna, gpt-6-astra and gpt-5.6-terra cases
* test(translation): rich azure_ai Claude cases for haiku-5-5 and fable-5-1
Bind the shared chat_completions, messages and responses client templates and the anthropic provider templates to azure_ai/claude-haiku-5-5 and azure_ai/claude-fable-5-1 from real Azure AI Foundry captures, keeping the Azure dialect exact (/anthropic/v1/messages, x-api-key plus api-key on chat_completions and responses, system as a text block list on /v1/messages). Add prompt_cache_hit variants for both models from the second call inside the cache window. The two deployments need rows in tests/integration/proxy_config.yaml
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(translation): rich Gemini bases for gemini-3.8-flash, gemini-3.1-pro-preview and gemini-4-argon
Add the shared generateContent provider templates in templates/gemini.py from
real captures and bind the three target models on chat completions, messages
and responses with explicit per-model response overrides. gemini-4-argon is
registered in the three basic lists. gemini-3.5-flash is unchanged
* test(translation): enrich vertex_ai claude and gemini cases from live captures
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(integration): rich Bedrock converse, invoke and mantle translation cases from real captures
* test(integration): register the rich translation deployments and follow the rich Gemini 3.8 Flash cases
Add the synthetic deployments the rich provider cases bind (anthropic, openai, azure, azure_ai, gemini, vertex_ai, bedrock converse, invoke and mantle) to tests/integration/proxy_config.yaml, all pointed at the fake provider on 127.0.0.1:8191 with synthetic keys. The Gemini omitted-temperature contract now takes its expected generationConfig and reply text from the rich GEMINI_3_8_FLASH_TEST_CASE literals instead of the retired minimal hello cases
* test(translation): reuse bridged and native response templates
---------
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: kerry <kerry@berri.ai>
* fix(bedrock): send redacted_thinking back to Converse as reasoningContent.redactedContent
Replaying an assistant turn that carried a redacted_thinking block to Bedrock
Converse turned it into an empty unsigned reasoningText, which the assistant
content builder then dropped as blank text. Bedrock returned 200 so callers
never noticed. Map redacted_thinking to redactedContent in both the
thinking_blocks and inline content paths of the sync and async builders
* fix(bedrock): drop blank redacted_thinking data and keep non-str signatures signed on Converse replay
* fix(bedrock): replay redacted_thinking to Converse only when the target is Claude
Nova, Llama, DeepSeek and gpt-oss reject reasoningContent.redactedContent with a 400, so a Claude
history replayed to them through a Router fallback or model switch keeps dropping the redacted block
the way main does. Claude is resolved from the Bedrock base model of the target, and an application
inference profile ARN counts as Claude when its deployment's base_model names a Claude model.
Adds integration contracts for the replayed Converse request shape, stream and non-stream, with a
tool call, and for non-Claude and ARN targets.
* test(e2e): cover Bedrock Converse replaying redacted_thinking live through the proxy
Turn 1 triggers redacted thinking on Claude Sonnet 4.6, turn 2 replays it, and a live
edge between the proxy and Bedrock checks the replayed Converse body carries the same
reasoningContent.redactedContent. Covers nonstream, stream and a tool call round trip
* fix(bedrock): validate litellm_params before reading the deployment base_model
litellm_params arrives as an untyped dict, so passing it straight into the typed
helper grew reportUnknownArgumentType. Validating it into Mapping[str, object] first
keeps the read typed without growing the type-check gate
* fix(bedrock): wrap non-Converse redacted_thinking data and let a named model id win over base_model
* test(bedrock): cover redacted_thinking minted outside Converse, model_id and the sync Router base_model path
* fix(bedrock): match bare claude- base_model names without reading the untyped cost map
* fix(bedrock): keep redacted_thinking data as Claude's own string on every route
Converse hands redactedContent back base64-wrapped, and that wrapper used to leak out as data,
so a Converse turn replayed to Invoke or the Anthropic API was rejected. Converse replies now
decode it, Converse requests wrap it exactly once, and the Anthropic-shaped builders unwrap
histories saved with the old wrapping. Responses API encrypted reasoning is no longer sent to
Claude on Converse, which rejected it with a 400
* fix(anthropic): keep tool_search_tool_result so the search call replays with its result
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(anthropic): drop a client tool_result for a search call replayed with its server result
* fix(anthropic): replay a server tool call the client answered as the client sent it
* fix(anthropic): keep a server tool result under modify_params, count client-answered marks, and replay it from run_tool_loop
modify_params no longer adds a placeholder answer for a server tool call whose result is kept, unless the call ends the request. The cache control hook counts a mark on a server tool call the client answered, since that call goes out as a plain tool_use. run_tool_loop sends provider_specific_fields back and runs only the client's tool calls.
* fix(anthropic): keep a server tool result when another tool answer follows, scope run_tool_loop's replay, and keep code_interpreter_results off tool search turns
* test(anthropic): audit cells for tool search result round trips, replay, cache marks, pass-through logging and chaos
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(router): read the first stream chunk through __aiter__ so wrappers that hand iteration off (MCPStreamWrapper) stream again
* fix(router): mark the _RestT TypeVar Final for the type discipline gate
---------
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses bridge): keep defer_loading on function tools sent to the Responses API
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses bridge): give the defer_loading casts a cast-ok reason
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(responses bridge): build defer_loading outside the tool loop so Final stays valid
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses bridge): honor drop_params for defer_loading and route tool_search to Responses
Under drop_params, defer_loading is dropped when no tool_search tool is sent,
so the 400 it would cause upstream goes away as before. A non-bool value answers
400 without drop_params and is dropped with it. A null tool-level value falls
back to the nested function value. A request carrying a tool_search tool now
bridges to Responses on OpenAI and Azure endpoints even with reasoning_effort
none, since chat completions rejects the tool_search type.
* fix(responses bridge): forward defer_loading only beside a tool_search tool
defer_loading only means something with tool_search, and backends without it
reject the key (xAI answers 403). A true value with no tool_search tool now
answers 400 naming drop_params and is dropped under drop_params; a false value
there is a no-op and is dropped.
* fix(responses bridge): drop defer_loading beside a client-executed tool_search
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(caching): report zero response_cost on a response-cache hit
* test(caching): cover response_cost on a cache hit
* fix(caching): pass cache_hit when stamping a cached response's metadata
* feat(ui): replace deployment warning banners with a health icon, critical row and panel
The debug, no Redis, env credential login and license expiry banners are gone.
Proxy admins and proxy admin viewers now get a shield icon in the header with
an active warning count, one row under the header for the top critical warning,
and a side panel where proxy admins snooze, ignore or restore each warning for
every admin of the proxy
* test(ui): cover deployment health wiring and warning content, drop unused license tier helper
Both dashboard shells are checked end to end for the icon, critical row and
panel, and the warning catalog is checked for its license wording, fix text
and links. getLicenseExpiryTier and its thresholds lost their last caller
with the license banner, and the proxy now decides severity
* fix(ui): wrap long setting names in deployment warning fix text on narrow screens
* style(ui): restyle deployment health to the dashboard design system
The critical row now follows the deployment alert banner: tone tint, bold lead, Review as a link and +N more on the right. The panel drops nested cards for ruled rows with severity icons, row actions move into a ⋯ menu, ignored rows show only who snoozed them and until when, and loading uses skeleton rows. The header shield matches the bell and carries a small count pill
* style(ui): rebuild deployment health on shadcn primitives with the reviewed design
The banner shows the most severe active alert by title with a Review link, the header shield carries a count badge, and the panel groups issues into collapsible Critical, Warnings, and Ignored and snoozed sections. Each issue is a tinted Alert with a docs link and a Snooze menu, snoozed issues keep the full layout with a status badge and Restore, and the empty state uses the shadcn Empty primitive
* chore(ui): exempt shadcn primitives from the PascalCase filename rule
The shadcn CLI writes kebab-case files under components/ui, so every primitive needed a suppression entry and each new one failed lint. Exempt the folder and drop the 34 entries it replaces
* fix(ui): let long setting names in deployment warnings wrap inside the alert on phones
The shadcn Alert lays out its text in a 1fr grid column, whose minimum width is the longest unbreakable run. overflow-wrap break-word does not lower that minimum, so a long setting name pushed the panel wider than a 390px screen. overflow-wrap anywhere does
